Functional Magnetic Resonance Imaging, or fMRI, includes imaging techniques that may be used to determine which areas of a patient's brain (or other tissues) are activated by different types of activities, such as the patient moving their fingers or reacting to a particular image. To execute an fMRI scan of the brain, for example, an MRI machine is first configured to image increased blood flow to activated brain areas. A task event is then provided that is likely to cause activity, and changes in blood flow, in the patient's brain. (Such events may include, for example, viewing pictures that may be selectively presented to the patient during the course of the imaging, naming exemplars within a category of items, solving a math problem, making a specific movement, etc.). A high-resolution anatomical scan is taken of the patient's brain for later use in creating a background for a composite image showing the activated areas of the patient's brain. A series of low spatial but high temporal resolution functional scans are then taken of the patient's brain over time (e.g., 6 whole-brain scans taken every 10 seconds). During this process, the event is present for some of the scans, and absent in others. For technical reasons, these images are acquired in “k-space” format, which must be subjected to Fourier transforms to be visualized.
After transforming these images and correcting for distortions, statistical analysis (such as t-tests or deconvolution multiple regression) compares the “with event” scans to the “without event” scans to determine which parts of the brain were activated reliably by the event. The reliably activated areas (as measured in the low spatial resolution functional scans) are superimposed in color on top of the high-resolution anatomical scan of the patient's brain. This composite image may be viewed in 3D, and typically may be viewed from any angle. This presents a visual indication of exactly which areas of the brain were activated by the specified event (e.g., stimulus perception, word finding, problem solving, or action).
The basis for the fMRI process generates blood-oxygenation-level-dependent (BOLD) and blood-oxygenation-steady-state (BOSS) contrasts. Oxyhemoglobin, like water and brain tissue, is diamagnetic (negative magnetic susceptibility) while deoxyhemoglobin is paramagnetic (small positive susceptibility). The ratio of deoxy- to oxyhemoglobin in a blood vessel affects the local magnetic field, which in turn affects the precession frequencies of local water protons exposed to the strong main magnetic field used for MRI. BOSS contrast involves measuring this frequency spectrum (analogous to FM radio). Changes in precession frequencies alter the ability to rephase the protons' radio frequency (RF) signals, which are spatially encoded by magnetic gradients. BOLD contrast involves measuring signal amplitudes, which are affected by de- and re-phasing (analogous to AM radio). At a place in the brain where increased neural activity's metabolic demands convert oxy- to deoxyhemoglobin, there is a reduction in RF signal strength through reduced rephasing as the RF spectrum shifts. Within a few seconds an influx of blood oversupplies this locale with new oxyhemoglobin, yielding a relatively large signal increase and spectral shift. After the neural activity ends, the RF signal gradually decays toward the pre-activity level. This time-varying signal, known as a hemodynamic response, is the fMRI signature from which underlying neural activity is inferred.
Calibration within the field of fMRI is challenging because repeatability is imperfect—even when using the same procedures with the same subject. Calibrations of fMRI instruments have previously been done using phantoms filled with aqueous solutions or gels. Ordinary engineering calibrations of the signal-to-noise ratio in such phantoms capture the reliability of spatially encoding the fMRI data into “k-space” (that is, encoding spatial frequency or “where” activity happened) without addressing contrast-to-noise reliability for fMRI applications (that is, “what” activity happened).
It is difficult to establish a calibration standard for in vivo fMRI data. In this regard, repeatable data are necessary for calibration, but living participants introduce multiple sources of variance into the measurements. In particular, the presence of instrument noise and “physiological noise” produced by a living human brain (or by extension, produced by other living tissue) affects the repeatability of acquiring BOLD/BOSS contrast signals. In addition, there may be many other sources of measurement variability, such as the anatomical differences of different human subjects, irreproducible involuntary head motion artifacts, and the ongoing metabolic activities of the brain. Thus, current techniques for calibrating the sensitivity and specificity of an fMRI test for detecting BOLD/BOSS contrast signals in the presence of such variable and idiosyncratic noise remain inadequate. Sensitivity and specificity are important figures of merit for test reliability that are widely adopted for evaluating medical tests, because many physicians rely upon sensitivity and specificity indices to interpret test results for individual patients. A further important figure of merit known as receiver operating characteristic (ROC) combines the information from sensitivity and specificity.
Therefore, there is a need for improved systems and methods for calibrating fMRI. Namely, there is a need for systems and methods that calibrate fMRI test results by taking into account various in vivo signals as well as instrument noise, to establish figures of merit for the test results including sensitivity, specificity, and ROC.